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Case Studies

Scaling ETL Workloads with Kubernetes and RabbitMQ Autoscaler

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Scaled ETL workloads using Kubernetes and RabbitMQ Autoscaler, enabling dynamic scaling, faster processing, and stable performance during peak demand.
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Client Overview

Early-stage B2B SaaS company (NDA) Team size: 8 engineers Stack: Node.js, React, PostgreSQL, Redis Cloud: AWS

The Challenge

The client was deploying services manually to EC2 instances. This led to:

  • Frequent downtime during deployments
  • Inconsistent environments between staging and production
  • Manual scaling and no self-healing
  • Lack of visibility and monitoring for workloads

They needed a reliable, scalable solution that supported fast development cycles without hiring a full-time DevOps team.

The Solution: Kubernetes on AWS (EKS)

CDOps Tech team migrated the infrastructure to Amazon EKS and restructured the deployment process using GitOps and Infrastructure as Code.

Key implementations:

  • Provisioned EKS using Terraform with reusable modules
  • Dockerized all services and configured Kubernetes manifests (Deployments, Services, Ingress)
  • Set up Helm charts for repeatable, templated deployments
  • Integrated ArgoCD for GitOps-based deployment
  • Enabled Horizontal Pod Autoscaling and self-healing
  • Added Prometheus + Grafana for cluster-level monitoring
  • Centralized logging via CloudWatch Logs
case study 1

The Results

  • Achieved 99.95% uptime in the first 90 days post-migration
  • Reduced release friction, developers now deploy multiple times per day
  • Infrastructure now scales automatically with traffic
  • No additional full-time DevOps hires required

Want to deploy with confidence using Kubernetes?

Let’s discuss how I can help your team. Book your free consultation here.

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